Language-Grounded Indoor 3D Semantic Segmentation in the Wild (2204.07761v2)

Abstract: Recent advances in 3D semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets. However, current 3D semantic segmentation benchmarks contain only a small number of categories -- less than 30 for ScanNet and SemanticKITTI, for instance, which are not enough to reflect the diversity of real environments (e.g., semantic image understanding covers hundreds to thousands of classes). Thus, we propose to study a larger vocabulary for 3D semantic segmentation with a new extended benchmark on ScanNet data with 200 class categories, an order of magnitude more than previously studied. This large number of class categories also induces a large natural class imbalance, both of which are challenging for existing 3D semantic segmentation methods. To learn more robust 3D features in this context, we propose a language-driven pre-training method to encourage learned 3D features that might have limited training examples to lie close to their pre-trained text embeddings. Extensive experiments show that our approach consistently outperforms state-of-the-art 3D pre-training for 3D semantic segmentation on our proposed benchmark (+9% relative mIoU), including limited-data scenarios with +25% relative mIoU using only 5% annotations.

• The paper introduces ScanNet200, a benchmark with 200 object classes that significantly expands the scope of indoor 3D semantic segmentation.
• The paper presents a language-grounded pre-training method using text embeddings from CLIP to robustly align 3D features with linguistic data.
• The paper achieves a 9% overall mIoU improvement and a 25% increase with limited annotations, demonstrating its effectiveness in real-world scenarios.

Language-Grounded Indoor 3D Semantic Segmentation in the Wild

The research presented in the paper focuses on a critical enhancement in the domain of 3D semantic segmentation. The paper addresses the limitations inherent in previous benchmarks and proposes an advanced method that integrates LLMs for robust Indoor 3D semantic segmentation. Specifically, the authors introduce ScanNet200, a benchmark that extends the categories for evaluation to 200 classes, significantly surpassing the existing benchmarks that consider fewer than 30 categories. This increase in granularity is crucial for capturing the diversity and complexity of real-world environments.

Core Contributions

The paper makes several key contributions to the field of 3D semantic segmentation:

1. ScanNet200 Benchmark: The authors propose a 200-class 3D semantic segmentation benchmark, extending the existing ScanNet dataset. This new benchmark includes a wider variety of object categories, addressing natural class imbalances present in real-world data.
2. Language-Grounded Pre-Training: To manage the expanded class vocabulary and the associated challenges of class imbalance and limited data scenarios, the paper introduces a language-driven approach. This involves pre-training 3D features using text embeddings from the pre-trained CLIP model. The pre-training process aligns 3D features with text embeddings in a shared space using a contrastive loss, enabling robust feature learning.
3. Instance-Based Sampling and Class-Balanced Loss: The authors propose instance-based data augmentation and a class-balanced focal loss, further improving segmentation performance. Instance-based sampling augments training data by introducing instances of rarely seen categories into scenes, mitigating class imbalance. The class-balanced focal loss provides dynamic re-weighting, focusing learning on under-represented classes.

Experimental Results

The experimental evaluation demonstrates the effectiveness of the proposed methods. The authors report significant improvements over state-of-the-art approaches:

• A +9% relative improvement in the mean Intersection over Union (mIoU) metric across the 200 classes when compared to baseline 3D pre-training methods.
• In scenarios with limited annotations, using only 5% of provided annotations, the proposed method achieved a +25% relative mIoU improvement.
• The segmentation performance in challenging real-world conditions, such as class imbalance and limited data, also showed substantial improvement.

Furthermore, the paper demonstrates the robustness of the proposed language-grounded feature learning approach across practical applications, showcasing its utility in various downstream tasks, including 3D instance segmentation.

Implications and Future Directions

The implications of this research are multi-faceted. Practically, the ScanNet200 benchmark sets a new standard for evaluating 3D semantic segmentation models, encouraging future research to address a larger vocabulary of classes. Theoretically, the integration of LLMs such as CLIP into 3D feature learning signifies a promising direction towards multi-modal learning that leverages rich, pre-trained linguistic knowledge to enhance visual-semantic understanding.

The research could be further extended by incorporating additional modalities such as high-resolution color images to provide richer signals for small and infrequent objects. Moreover, leveraging advanced natural language processing techniques to incorporate more comprehensive textual descriptions or attributes could refine the learning process and potentially lead to even more robust 3D feature representations.

Conclusion

This research makes significant strides in 3D semantic segmentation by addressing scalability, robustness to class imbalance, and data efficiency. The introduction of the ScanNet200 benchmark, combined with innovative pre-training methods and class balancing strategies, marks a substantial advancement in the field. As a result, this work lays a strong foundation for future research aimed at achieving more robust and comprehensive 3D semantic scene understanding in diverse real-world applications.